Neural network learning algorithm for a class of interconnected nonlinear systems

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2008.03.013

Title:	Neural network learning algorithm for a class of interconnected nonlinear systems
Authors:	Huang, S.N. Tan, K.K. Lee, T.H.
Keywords:	Adaptive control Neural network learning Nonlinear systems
Issue Date:	Jan-2009
Citation:	Huang, S.N., Tan, K.K., Lee, T.H. (2009-01). Neural network learning algorithm for a class of interconnected nonlinear systems. Neurocomputing 72 (4-6) : 1071-1077. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2008.03.013
Abstract:	In this paper, an adaptive neural network algorithm is developed for a class of interconnected nonlinear systems. Neural networks (NNs) are used to approximate the unknown nonlinear functions and interconnections in the subsystems. A systematic approach is established to synthesize the adaptive NN learning control scheme that ensures the boundedness of all the signals in the closed-loop system. The effectiveness of the proposed scheme is demonstrated by computer simulations. © 2008 Elsevier B.V. All rights reserved.
Source Title:	Neurocomputing
URI:	http://scholarbank.nus.edu.sg/handle/10635/56783
ISSN:	09252312
DOI:	10.1016/j.neucom.2008.03.013
Appears in Collections:	Staff Publications

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